1. Field of the Invention
The present invention generally relates to a recognizing apparatus for recognizing a class to which an inputted characteristic pattern belongs, and more particularly, to a recognizing apparatus for recognizing the class from among a plurality of classes to be discriminated by using a neural network appropriate to a category to which the class belongs.
2. Description of the Prior Art
In some of the conventional recognizing apparatus, classes to which objects to be discriminated belong are not classified into categories. A class containing inputted data is generally selected from among all the classes.
In other recognition apparatus, all the classes are initially classified into categories. In this case, inputted data is compared with each of several reference patterns contained in each category so that a category to which the inputted data belongs may be discriminated from among all the categories. The class containing the inputted data is then selected from among the classes contained in the discriminated category.
In either case, however, a single neural network is used in selecting the target class.
Each of the neural networks generally has a learning algorithm whereby the operation for modifying various internal connection factors (weights) is repeated in accordance with given learning data so that the internal weights may be automatically converged into respective values appropriate for discriminating the learning data. This learning algorithm is applicable to the recognizing apparatus. In such a case, the recognizing apparatus learns to discriminate a class to which given learning data belongs from among all the classes to be discriminated. As a result, the learned neural network outputs a class to which an inputted data belongs in accordance with weights set in the learning.
There exist various learning algorithms such as Backpropagation network, Learning Vector Quantization (LVQ2) etc. However, each of the various learning algorithms has advantages and disadvantages. Accordingly, the most appropriate neural network must be selected so that the learning algorithm or discriminating algorithm contained therein may be suited for the contents of the class to be discriminated. If the appropriate neural network is not used, the learning or recognition is disadvantageously lowered in efficiency, or the recognition performance is lowered.